Machine learning generally refers to a field of computer science that is focused on enabling machines such as computers to learn without being explicitly programmed. Machine learning includes the study and construction of machine-performed algorithms or techniques that enable machines to learn from and make predictions on data. In particular, such algorithms can operate by building a model from a training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static programming instructions.
One main branch of machine learning techniques includes supervised learning techniques. Supervised learning can include inferring or learning a function or model from a training data set that includes a number of labeled examples. For example, each example in the training data set can include one or more input values (which can be expressed as a vector with a number of features) and one or more desired output values (which can also be called supervisory signals). Typically, training data is labeled using known ground truth information that provides the input values and the output values. A supervised machine learning algorithm can analyze the training data and produce an inferred model, which can then be used for mapping or making predictions on new, unlabeled examples. However, in many scenarios, labeled data and/or ground truth data can be difficult and/or costly to obtain. This can be the case for a machine-learned model of a communication channel, which is often subject to changing channel conditions. Thus, performing supervised learning in such scenarios can be difficult or impossible.